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Detection and visualization of misleading content on Twitter

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Abstract

The problems of online misinformation and fake news have gained increasing prominence in an age where user-generated content and social media platforms are key forces in the shaping and diffusion of news stories. Unreliable information and misleading content are often posted and widely disseminated through popular social media platforms such as Twitter and Facebook. As a result, journalists and editors are in need of new tools that can help them speed up the verification process for content that is sourced from social media. Motivated by this need, in this paper, we present a system that supports the automatic classification of multimedia Twitter posts into credible or misleading. The system leverages credibility-oriented features extracted from the tweet and the user who published it, and trains a two-step classification model based on a novel semisupervised learning scheme. The latter uses the agreement between two independent pretrained models on new posts as guiding signals for retraining the classification model. We analyze a large labeled dataset of tweets that shared debunked fake and confirmed real images and videos, and show that integrating the newly proposed features, and making use of bagging in the initial classifiers and of the semisupervised learning scheme, significantly improves classification accuracy. Moreover, we present a Web-based application for visualizing and communicating the classification results to end users.

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Notes

  1. snopes.com/photos/airplane/malaysia.asp.

  2. www.theguardian.com/business/2013/apr/23/ap-tweet-hack-wall-street-freefall.

  3. github.com/MKLab-ITI/computational-verification.

  4. github.com/MKLab-ITI/image-verification-corpus.

  5. code.google.com/p/language-detection/.

  6. github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107.

  7. onlineslangdictionary.com/word-list/0-a/.

  8. www.languagerealm.com/spanish/spanishslang.php.

  9. github.com/ipeirotis/ReadabilityMetrics.

  10. www.mywot.com/.

  11. wwwranking.webdatacommons.org/more.html.

  12. data.alexa.com/data?cli=10&url=google.com.

  13. Using: github.com/socialsensor/geo-util.

  14. The selection is based on their performance on the training set during cross-validation.

  15. reveal-mklab.iti.gr/reveal/fake/.

  16. The VC was since then expanded with new data that was used as part of the VMU 2016 task. However, the bulk of the experiments reported here refer to the 2015 version of the data, so any reference to VC refers to the 2015 edition of the dataset, unless otherwise stated.

  17. In MediaEval, each team can submit up to five runs.

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Author information

Authors and Affiliations

  1. Urban Big Data Centre, Glasgow, UK

    Christina Boididou

  2. CERTH-ITI, Thessaloniki, Greece

    Christina Boididou, Symeon Papadopoulos, Markos Zampoglou, Lazaros Apostolidis, Olga Papadopoulou & Yiannis Kompatsiaris

Authors
  1. Christina Boididou
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  2. Symeon Papadopoulos
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  3. Markos Zampoglou
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  4. Lazaros Apostolidis
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  5. Olga Papadopoulou
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  6. Yiannis Kompatsiaris
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Corresponding author

Correspondence to Symeon Papadopoulos.

Additional information

This work has been supported by the REVEAL and InVID projects, under Contract Nos. 610928 and 687786, respectively, funded by the European Commission.

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Boididou, C., Papadopoulos, S., Zampoglou, M. et al. Detection and visualization of misleading content on Twitter. Int J Multimed Info Retr 7, 71–86 (2018). https://doi.org/10.1007/s13735-017-0143-x

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  • DOI: https://doi.org/10.1007/s13735-017-0143-x

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